Abstract:

Many bio-inspired algorithms (evolutionary algorithms, artificial
immune systems, particle swarm optimisation, ant colony optimisation, )
are based on populations of agents.
Stepney
et
al
[2005]
argue for the use of conceptual frameworks and
meta-frameworks to capture the principles and commonalities underlying
these, and other bio-inspired algorithms. Here we outline a generic
framework that captures a collection of population-based algorithms,
allowing commonalities to be factored out, and properties previously
thought particular to one class of algorithms to be applied uniformly
across all the algorithms. We then describe a prototype proof-of-concept
implementation of this framework on a small grid of FPGA (field
programmable gate array) chips, thus demonstrating a generic
architecture for both parallelism (on a single chip) and distribution
(across the grid of chips) of the algorithms.